Blockchain based secure, efficient and coordinated energy trading and data sharing between electric vehicles

In this study, a secure and coordinated blockchain based energy trading system for Electric Vehicles (EVs) is presented. The major goals of this study are to provide secure and efficient energy trading between EVs and Charging Stations (CSs), and to ensure efficient coordination between EVs. In this study, a consortium blockchain based energy trading algorithm is presented that handles the essential energy requests, discards the redundant requests and calculates the distance between EVs and CSs. Moreover, Matching Pool (M-Pool) and Pairing Pool (P-Pool) are used to store energy and payment requests. Furthermore, a blockchain based mechanism is proposed for ensuring efficient coordination between EVs at the unsignalized crossroads and intersections. The scheme involves efficient communication between EVs and the timely sharing of important messages. In the proposed work, EVs are authenticated using a Registration Authority (RA) before they become part of a Vehicular Energy Network (VEN). The increase in the number of EVs results in an increase in the number of messages leading to data redundancy issue. To solve the issue, message filtration is performed. The delays incurred in the VEN are also mathematically formulated in this study. In addition, the range anxiety issue is discussed in the proposed work. Besides, a Local Aggregator (LAG) is used as an energy broker to manage energy trading events and transaction validation. To promote the users to take part in the proposed network, they are provided with incentives. The proposed model is tested against selfish mining attack and the security analysis is performed through Oyente tool. The simulation results show that the proposed study excels in providing a secure, efficient and coordinated energy trading and data sharing system for EVs. The results show that due to proper coordination, the risk factor is reduced by almost 25-30%. Moreover, almost 40-50% reduction in time is observed when storing less redundant data.